LEARNING OUTCOMES

The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes.

AIMS AND LEARNING OUTCOMES

The first part of the course involves the crucial aspected related to the processing of time series, the Bayesian analysis and filtering and the basics of pattern recognition. The second part will discuss the main predictive approaches, such as regularization theory, machine and deep learning. The teaching approach will combine the description of the main theoretical aspects of data analysis with some focus on applications in economics and applied sciences

At the end of the course the students will gain some insights in the computational data analysis world and in the comprehension of some aspects of artificial intelligence. Further, they will obtain skills concerning the computational tools for the processing of data and time series with predictive purposes

PREREQUISITES

R or Matlab programming

Data formats and I/O issues

Basic aspects of numerical analysis and statistics

TEACHING METHODS

Frontal teaching and computer projects

SYLLABUS/CONTENT

Data analysis:

time series

Bayesian filtering

Pattern recognition

Prediction from data:

regularization theory

neural networks

Bayesian approaches

deep learning

RECOMMENDED READING/BIBLIOGRAPHY

Handouts